Can Sustainable Investment Yield Better Financial Returns: A Comparative Study of ESG Indices and MSCI Indices

‘Sustainable investment’—includes a variety of asset classes selected while caring for the causes of environmental, social, and governance (ESG). It is an investment strategy that seeks to combine social and/ or environmental benefits with financial returns, thus linking investor’s social, ethical, ecological and economic concerns Under certain conditions, these indices also help to attract foreign capital, seeking international participation in the local capital markets. The purpose of this paper is to study whether the sustainable investment alternatives offer better financial returns than the conventional indices from both developed and emerging markets. With an intent to maintain consistency, this paper comparatively analyzes the financial returns of the Thomson Reuters/S-Network global indices, namely the developed markets (excluding US) ESG index—TRESGDX, emerging markets ESG index—TRESGEX, US large-cap ESG index—TRESGUS, Europe ESG index—TRESGEU, and those of the usual markets, namely MSCI world index (MSCI W), MSCI All Country World Equity index (MSCI ACWI), MSCI USA index (MSCI USA), and MSCI Europe Australasia Far East index (MSCI EAFE), MSCI Emerging Markets index (MSCI EM) and MSCI Europe index (MSCI EU). The study also focusses on the inter-linkages between these indices. Daily closing prices of all the benchmark indices are taken for the five-year period of January 2013⁻December 2017. Line charts and unit-root tests are applied to check the stationary nature of the series; Granger’s causality model, auto-regressive conditional heteroskedasticity (ARCH)-GARCH type modelling is performed to find out the linkages between the markets under study followed by the Johansen’s cointegration test and the Vector Error Correction Model to test the volatility spillover between the sustainable indices and the conventional indices. The study finds that the sustainable indices and the conventional indices are integrated and there is a flow of information between the two investment avenues. The results indicate that there is no significant difference in the performance between sustainable indices and the traditional conventional indices, being a good substitute to the latter. Hence, the financial/investment managers can obtain more insights regarding investment decisions, and the study further suggests that their portfolios should consider both the indices with the perspective of diversifying the risk and hedging, and reap benefits of the same. Additionally, corporate executives shall use it to benchmark their own performance against peers and track news as well

Can Central Banking Policies Make a Difference in Financial Market Performance in Emerging Economies? The Case of India

This paper explores the importance of central banking policies in financial market performance, using the case of India. For this purpose, the paper comparatively analyzes the performance of financial markets during the regimes of last three governors of the Reserve Bank of India—Y V Reddy, D Subbarao, and Raghuram Rajan. The paper discusses the central banking policies in these periods with respect to monetary stability, inflation, and growth challenges. The paper presents an analysis of returns and volatility in stock markets and currency markets in their tenures in comparison with those from other selected emerging markets (Brazil, Russia, China, South Africa) and developed markets (USA and UK). The paper also brings out the leverage effect by applying the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model in addition to comparatively analyzing the performance of financial markets. Further, the paper assesses the impact of central banking policies on financial markets by using the fixed effect model on the reference countries for the period under reference

Biological Control of Agricultural Insect Pests

Pests are highly responsible for heavy crop losses and reduced food supplies, poorer quality of agricultural products, economic hardship for growers and processor. Generally, chemical control methods are practiced for their control which is neither always economical nor effective and may have associated unwanted health, safety and environmental risks. However, to meet the challenge of feeding to the ever increasing human population, an efficient, economical and environment friendly disease control methods are requisites. In this regard, biological control may be an effective means of reducing or mitigating the pests and pest effects through the use of natural enemies. Biological control is an environmentally sound which involves the use of beneficial microorganism to control plant pathogens and diseases they cause. Therefore, in this chapter we will provide a comprehensive account of this environmental friendly approach for effectively management of plant diseases. This chapter will also accentuate the development of biological control agents for practical applications and the underlying mechanism. The contents in the chapter will be beneficial and advantageous to all those working in academia or industry related to crop protection

Neuroentrepreneurship: an integrative review and research agenda

There is emergent literature that converges from neuroscience and entrepreneurship research, but the definitions and interlinkages are still inconsistent. We conduct a systematic literature review of 167 papers on the interface between neuroscience and entrepreneurship to address this. We observe the literature trends examining the interlinkages between neuroscience and entrepreneurial intention through six antecedents, namely - molecular neuroscience, systems neuroscience, behavioral neuroscience, cognitive neuroscience, social neuroscience, and computational neuroscience. Our findings suggest that entrepreneurial intention impacts entrepreneurial activity through five factors, including (1) opportunity recognition, (2) evaluation and risk-taking, (3) entrepreneurial cognition, (4) entrepreneurial behavior, and (5) entrepreneurial decision-making. From our discussions, the links among the neural factors affecting entrepreneurship are identified, and a research agenda highlighting a pathway for future studies is proposed

Effect of auxin and gibberellic acid on growth and yield components of linseed (Linum usitatissimum L.)

The commercial importance of linseed (Linum usitatissimum L.) has attracted breeders to increase its seed yield usingvarious breeding approaches. Plant growth regulators (PGRs) have a significant role in enhancing yield and its related traits in linseed.In the present study, two plant growth hormones, auxin and gibberellic acid, were applied individually, as well as in combinations, inorder to study their effect on yield and its components in “Neelam”, which is a high yielding variety of linseed. A comparative studywas done under pot and field condition. A combined dose of auxin (1.0 mg L-1) and gibberellin (200 mg L-1) is recommended for theenhancement of seed yield, whereas a 0.5 mg L-1 dose of auxin is recommended for the enhancement of vegetative growth. It was concludedthat the plant growth regulators can be successfully employed to enhance the yield in this economically important oil seed crop

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject